SF Compute addresses GPU demand faster with Lightfield
SF Compute provides access to large-scale GPU clusters without forcing teams to overbuy or take on catastrophic balance-sheet risk. Its contracts are among the only in the industry that allow customers to sublease reserved capacity when plans change. Supporting that model means understanding each customer's exact compute needs, then matching that demand against available capacity.
They provide a critical service in a market where demand currently outpaces supply and securing the wrong capacity is one of the most expensive mistakes an AI start up, lab, or enterprise can make.
Demand moved faster than the CRM could capture it
Inbound buyers often arrive with highly specific requirements: GPU SKU, GPU count, storage, networking, managed services needs, and preferred contract terms. Montana Showalter, SF Compute's Head of Go-To-Market, owns how the company responds to that demand.
The commercial team is scaling fast against a steady stream of these inbound requests. Every deal depends on precise technical detail pulled from calls, email threads, and Slack conversations. Speed is also critical here as many customers are looking for GPUs on a compressed timeline. A slow intake process would make it difficult to determine which requests the company can serve now and which recurring gaps should influence the capacity SF Compute procures next.
That urgency pushed the team toward logging requests in Google Sheets instead of their CRM. It was faster to type a customer’s requirements into a row than to create and update the corresponding records in their previous CRM.
This solved the immediate data-entry problem but created another one. Customer emails, calls, relationships, capacity information, and deal outcomes remained scattered across other systems. The sales team could no longer connect demand to full customer history or easily see what the market was repeatedly asking for.
A record fast and precise enough that the team actually uses it
After importing SF Compute's history from its previous CRM into Lightfield, the team configured the pipeline around the exact information required to sell its large-scale GPU clusters. Each opportunity automatically captures the GPU SKU, GPU count, ideal cluster start date, and other technical requirements that determine whether SF Compute can or should serve the customer. Lightfield extracts those details from customer interactions and populates them into opportunities with no manual data entry.
Account executives can then use chat to query demand requirements across the entire customer base, update records, and surface new opportunities with similar customer profiles.
This has changed how the GTM team works. A pipeline review no longer begins with a spreadsheet stripped of customer history. Each opportunity sits alongside the calls, emails, contacts, requirements, and prior decisions that explain it. Reps can update a record, analyze recent calls, or investigate a pattern in demand without first assembling context from separate systems.. They’re also no longer slowed down by logging data into the CRM.
“We're able to track and close large deals faster with Lightfield because it surfaces critical customer requests and trends for the whole team. That automation and insight is extremely powerful.” Montana shared.
Extracting market signal from pipeline data
Once SF Compute’s opportunity history was connected, the GTM team could analyze demand across every deal rather than inspect opportunities one at a time.
This also allows the company to ask questions that help inform strategic supply decisions. From chat, they can pull up which customers asked for H100s in the last month or who's weighing GB300s against B300s, along with the reasoning each gave for preferring one SKU over another.
Lightfield provides a full breakdown segmented by technical requirements, GPU count, or customer profile. This is all grounded in the actual conversations with customers, not what somebody remembered to log. It turns unorganized demand signals into a clear view of where the market is heading.
“Those breakdowns have been helpful for the hardware and procurement team to understand where they should target their attention,” the team explained. The reports show where demand is concentrating across hardware configurations, customer segments, and workload types, so the team can make deliberate decisions about which clusters to source next.
Making customer intelligence part of the company’s operating rhythm
The team is now building recurring reports that show which GPU SKUs, cluster sizes, and types of workloads are driving demand.They’re also working to further automate how new demand enters the CRM. This includes automatic opportunity generation, turning Slack conversations into customer records. Together, these workflows will give SF Compute a more complete view of demand.
Lightfield gives SF Compute a faster customer learning loop. Each conversation adds context to the record and sharpens their understanding of what customers need. They can respond to every request with more context, so each interaction makes the next response better informed and helps SF Compute track where its customers are headed.